Synaptics (SYNA) Accumulated Expenses (2019 - 2026)
Synaptics (SYNA) posted Accumulated Expenses of $147.4 million for fiscal Q1 2027 (quarter ended Sep 26, 2026), down 13.0% from $169.5 million a year earlier and down 17.7% from the prior quarter.
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Analysis
Synaptics (SYNA) Accumulated Expenses (2019 - 2026) Analysis & Trends
At the end of FY2026 (ended Jun 27, 2026), Synaptics' Accumulated Expenses came in at $179.1 million, up 3.9% from FY2025.
- Annual Accumulated Expenses shows a five-year compound annual growth rate of 57.6% (FY2021 to FY2026).
- In prior fiscal years, Synaptics' Accumulated Expenses was $172.4 million in FY2025 (-6.2%), $183.7 million in FY2024 (+69.5%), $108.4 million in FY2023 (-25.4%) and $145.3 million in FY2022 (+689.7%).
- The fiscal Q1 2027 figure stands as the lowest quarterly Accumulated Expenses since fiscal Q3 2025.
- On a year-over-year basis, Accumulated Expenses increased in four of the last six quarters, with growth averaging 18.2%.
- The strongest year-over-year quarter for Accumulated Expenses in the past five years was fiscal Q4 2022, with growth of 689.7%; the weakest was fiscal Q4 2023, with a decline of 25.4%.
- According to Business Quant data, Accumulated Expenses for the three prior fiscal quarters was $179.1 million (Q4 2026), $170.5 million (Q3 2026) and $167.7 million (Q2 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | SK hynix | 24,980.27 Bn | 24,964.04 Bn | 43.99 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 10,337.48 Bn | 9,919.73 Bn | 31.04 Bn |
| 3 | Nvidia | 3,939.34 Bn | 3,713.50 Bn | 72.14 Bn |
| 4 | Broadcom | 1,653.85 Bn | 1,579.89 Bn | 20.46 Bn |
| 5 | Micron Technology | 880.27 Bn | 785.91 Bn | 47.05 Bn |
| 6 | Advanced Micro Devices | 875.86 Bn | 824.07 Bn | 7.21 Bn |
| 7 | Asml Holding | 576.26 Bn | 531.95 Bn | 5.90 Bn |
| 8 | Intel | 316.79 Bn | 179.50 Bn | 7.10 Bn |
| 9 | Applied Materials | 294.03 Bn | 294.03 Bn | 5.25 Bn |
| 10 | Synaptics | 4.69 Bn | 3.04 Bn | 140.40 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 26, 2026 | 147.40 Mn |
| Jun 27, 2026 | 179.10 Mn |
| Mar 28, 2026 | 170.50 Mn |
| Dec 27, 2025 | 167.70 Mn |
| Sep 27, 2025 | 169.50 Mn |
| Jun 28, 2025 | 172.40 Mn |
| Mar 29, 2025 | 110.80 Mn |
| Dec 28, 2024 | 114.60 Mn |
| Sep 28, 2024 | 136.40 Mn |
| Jun 29, 2024 | 183.70 Mn |
| Mar 30, 2024 | 98.80 Mn |
| Dec 30, 2023 | 103.90 Mn |
| Sep 30, 2023 | 103.00 Mn |
| Jun 24, 2023 | 108.40 Mn |
| Mar 25, 2023 | 93.70 Mn |
| Dec 24, 2022 | 113.10 Mn |
| Sep 24, 2022 | 128.20 Mn |
| Jun 25, 2022 | 145.30 Mn |
| Mar 26, 2022 | 15.70 Mn |
| Dec 25, 2021 | 7.90 Mn |
API Access
Synaptics Accumulated Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=SYNA&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-expenses", "ticker": "SYNA", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=accumulated-expenses&ticker=SYNA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();